Data Virtualization

What is Data Virtualization?
Data virtualization is a technology that allows organizations to access and integrate data from multiple sources into an abstracted view without having to move or replicate the data. Data virtualization enables users to create a “virtual layer” that can provide unified access to data located in disparate sources, such as databases, applications, and cloud services. By providing single-point access to data, data virtualization eliminates the need for ETL operations and enables real-time access to up-to-date information.

Benefits of Data Virtualization

1. Increased Agility: By virtualizing data, companies can quickly access the data they need without worrying about the complexities of physical infrastructure. This makes it easier to create and deploy applications faster and with fewer resources, resulting in increased agility for the organization as a whole.
2. Reduced Costs: Data virtualization eliminates the need for costly hardware and software, simplifying IT operations and reducing costs associated with physical infrastructure. It also reduces the need for manual processes that can be time-consuming and expensive.
3. Improved Data Accessibility: Data virtualization allows users to access data from multiple sources in real-time from any location. This means that users can get the information they need when they need it, regardless of where it is stored or how it is structured.
4. Improved Data Security: Data virtualization allows organizations to control access to sensitive data and ensure the security of their data. It also reduces the chances of data breaches or unauthorized access since there is no physical infrastructure that can be compromised.
5. Enhanced Data Analytics: By providing users with a single view of all their data, data virtualization simplifies the process of analyzing and interpreting large amounts of information. This makes it easier for organizations to gain insights from their data and make better decisions.
6. Improved Data Quality: Data virtualization helps organizations maintain the integrity of their data by allowing for better data cleansing and validation processes. This ensures that the data is accurate and up-to-date, which can help improve decision making

Challenges of Data Virtualization

1. Data Integration: Integrating data from disparate sources is one of the biggest challenges when it comes to data virtualization. The complexity of the data structures and integration processes involved can be difficult to manage and require significant resources.
2. Data Security: Since virtualized data is usually stored in a cloud environment, there is a risk of unauthorized access and potential security breaches. Organizations must ensure that their data is adequately protected from malicious actors or hackers.
3. Performance: The performance of virtualized data can be slower than traditional methods due to the added overhead associated with the virtualization process. This can lead to increased latency for certain applications or queries which may become an issue for time-sensitive operations.
4. Cost: Data virtualization requires additional hardware, software, and personnel, which can increase costs considerably. Organizations must carefully weigh the benefits of virtualizing their data against the associated financial costs.
5. Scalability: As data volumes increase, so does the need for scalability in order to maintain adequate performance. Data virtualization can provide a solution to this problem but can also be complex and costly to implement.

Data Virtualization Use Cases

1. Data Warehousing: Data virtualization can be used to create a unified view of data from multiple sources when building a data warehouse. It can help create a single view of disparate data from different systems and sources, reducing the complexity and cost of data integration.
2. Business Intelligence: Business intelligence teams can use data virtualization to access and analyze data from multiple sources in real time without having to physically move the data into one location or format. This reduces data latency and allows business intelligence teams to make more informed decisions faster.
3. Big Data Analytics: Big data analytics requires accessing large amounts of disparate data from different sources and combining them for analysis. Data virtualization simplifies this process by providing an easy way to access and aggregate all the required data in one place
4. Cloud Computing: Data virtualization can help move data stored in the cloud from various sources into a single, unified view. This can help reduce the complexity of managing and accessing data from multiple sources stored in the cloud.
5. Application Integration: Data virtualization can be used to integrate applications with disparate data sources without having to physically move or transform the data. This simplifies the process of integrating applications and makes it easier to access data from different sources.

Steps to Perform Data Virtualization

1. Assess the data landscape: Take inventory of the current data sources, assess the available data, and identify the most valuable sources of information.
2. Develop a data virtualization strategy: Create a strategy that outlines how data virtualization can be used to leverage existing data sources to create insights and improve operational efficiency.
3. Design the architecture: Design a logical and physical architecture for your data virtualization platform that will be able to handle the scale and complexity of your environment.
4. Implement security measures: Implement appropriate security measures to ensure that only authorized users have access to the data in your platform.
5. Configure and deploy the solution: Configure and deploy your solution according to best practices, ensuring that all components are working together as expected
6. Monitor and maintain the platform: Monitor the performance of your data virtualization platform, making sure to address any issues that arise.
7. Analyze and act on the insights: Analyze the data in your platform to gain insights, and take action on those insights to improve operational efficiency or create new opportunities.

Opensource Data Virtualization Tools

1. Apache Druid
2. Apache Kylin
3. Apache Tajo
4. Denodo
5. OpenLink Virtuoso
6. Talend Data Virtualization
7. Pentaho Data Integration
8. Red Hat JBoss Data Virtualization
9. Dremio
10. Microsoft SQL Server Polybase